Given a large dataset of unlabelled 3D medical images represented by raw voxels, there are many objective, data-driven ways to define the feature space on which clustering is performed (Table REF ) [1]}. A common method for feature extraction in medical imaging datasets is radiomic analysis [2]}, a quantitative imaging technique where large amounts of textural features are extracted from medical images. Traditional radiomic analyses rely on the extraction of hundreds of pre-specified quantitative features, which includes first-order histogram features, second-order grey-level co-occurrence matrices (GLCM), grey-level run-length matrices (GLRLM), fractal analyses, and others [3]}. This collection of radiomic features may not be the best representation of underlying disease abnormalities in medical imaging data, as these features are pre-specified and common across all disease types. In contrast, learned radiomics is an emerging subfield within radiomics [4]} that relies on the extraction of features which are learned from an autoencoder, a type of unsupervised machine learning architecture. The autoencoder learns the best representation of the data using a set of features, known as deep embeddings or latent features [1]}, which are in a reduced dimension compared to the size of the original image. Thus, learned radiomic features can be advantageous and more representative over traditional radiomic features for datasets with diseases that have not been classified by a domain-specific expert, or are too complex to be visually classified.
